1. Field of the Invention
The present invention relates to a feature extracting apparatus, a feature extracting method, an image processing apparatus, and a computer program for CALCULATING PIXEL FEATURES OF IMAGE.
2. Description of the Related Art
Techniques for extracting features of an image have conventionally been known. For example, a process of recognizing an object captured in an image includes two steps: extracting features from the image and recognizing the object using the extracted features. It is difficult to restore the information lost in the feature extracting process, which is previously performed, during the recognition process, which is subsequently performed. Thus, the feature extracting process, which is performed previously, is important to perform the recognition process properly.
A feature extracting method for recognizing human individuals and other objects in an image is disclosed by Navneet Dalal and Bill Triggs, “Histograms of oriented gradients for human detection,” the Institute of Electrical and Electronics Engineers (IEEE) Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) 2005, vol. 1, pp. 886-893, 2005, as an example of the feature extracting process. Dalal et al. discloses that the image is divided into a plurality of areas in a grid pattern so that the values of bins in a luminance gradient direction histogram calculated for each area are used as features.
JP-A 2000-207566 (KOKAI) and “A Novel Shape Feature for Image Classification and Retrieval” by Rami Rautkorpi and Jukka Iivarinen in Proceedings of the International Conference on Image Analysis and Recognition, Lecture Notes in Computer Science (LNCS) 3211, Part I, pages 753-760, Porto, Portugal, Sep. 29 to Oct. 1, 2004 disclose a feature extracting method used for classifying various types of texture images. According to JP-A 2000-207566 (KOKAI), a matrix (i.e., a co-occurrence matrix) is used as features. The matrix has elements comprises the number of combinations expressed as P(i, j), where “j” denotes a gray level of a point obtained as a result of a parallel translation, of a predetermined distance and in a predetermined direction, from a point within the image of which the gray level is “i”. In texture images, because similar patterns repeatedly appear at regular distance intervals, the features expressing a co-occurrence of gray-level values in two points that are positioned away from each other by the distance of the regular interval are effective in the recognition of the texture. According to Rautkorpi et al., it is possible to extract a feature that is robust even in changes caused by, for example, illuminations, using a co-occurrence matrix of luminance gradient directions, instead of the gray-level values of the image.
According to Dalal et al., however, the feature is extracted for each of the areas that are divided and arranged in the form of a grid. Thus, each of the features reflects only the information in the corresponding area. To perform the recognition process properly, it is necessary to use not only local information but also global information that expresses relationships with distant areas.
In addition, according to JP-A 2000-207566 (KOKAI) and Rautkorpi et al., because the texture images are used as targets, a recognition process performed on an object that has few repeatedly-appearing patterns (e.g., a person) is not taken into consideration.